提出机器人需具备自纠错能力以提升人机交互可靠性
Designing for Error Recovery in Human-Robot Interaction
- 从单次决策转向连续交互，强调错误检测与恢复机制
- 以核设施机械手套箱为场景，验证系统容错设计可行性
- 适合关注机器人鲁棒性与人机协作的工程研究者
本文探讨编程机器人人工智能系统的方式。当前多数AI系统追求超越人类表现的单一性能指标，但这些系统多聚焦于一次性、单向决策，而真实世界更具连续性和交互性。相比之下，人类能有效从错误中恢复并学习，从而实现更高成功率。本文聚焦构建可自我检测与修复错误的系统，以机器人在核设施机械手套箱中的应用为例，说明具体挑战，并提出初步设计思路。
原文摘要 · Abstract (English)
This position paper looks briefly at the way we attempt to program robotic AI systems. Many AI systems are based on the idea of trying to improve the performance of one individual system to beyond so-called human baselines. However, these systems often look at one shot and one-way decisions, whereas the real world is more continuous and interactive. Humans, however, are often able to recover from and learn from errors - enabling a much higher rate of success. We look at the challenges of building a system that can detect/recover from its own errors, using the example of robotic nuclear gloveboxes as a use case to help illustrate examples. We then go on to talk about simple starting designs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。